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• MongoDB equivalent of SQL "OR"

  - by Matt

  So, MongoDB defaults to "AND" when finding records. For example: db.users.find({age: {'$gte': 30}, {'$lte': 40}}); The above query finds users = 30 AND <= 40 years old. How would I find users <= 30 OR = 40 years old?

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• MongoDB PHP Drive install failed (Windows)

  - by terence410

  I attempted to download the php_mongo.dll from this link (http://github.com/mongodb/mongo-php-driver/downloads) and added the extension to php. However it doesn't work at all. My PHP version is: 5.2.x Windows: Windows 7 64 bit. I have also try to use pecl install mongo but it returned some error like: This DSP mongo.dsp doesn't not exist. Could somebody help?

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• Symfony (PHP framework) and MongoDB ( or any json-based database)

  - by fenderplayer

  I was wondering if its possible to use a json-based schema-free, document-based database like Mongodb or Couchdb on a symfony project like its used for ruby-on-rails websites? And if yes, how can it be done?

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• Indexing on only part of a field in MongoDB

  - by Rob Hoare

  Is there a way to create an index on only part of a field in MongoDB, for example on the first 10 characters? I couldn't find it documented (or asked about on here). The MySQL equivalent would be CREATE INDEX part_of_name ON customer (name(10));. Reason: I have a collection with a single field that varies in length from a few characters up to over 1000 characters, average 50 characters. As there are a hundred million or so documents it's going to be hard to fit the full index in memory (testing with 8% of the data the index is already 400MB, according to stats). Indexing just the first part of the field would reduce the index size by about 75%. In most cases the search term is quite short, it's not a full-text search. A work-around would be to add a second field of 10 (lowercased) characters for each item, index that, then add logic to filter the results if the search term is over ten characters (and that extra field is probably needed anyway for case-insensitive searches, unless anybody has a better way). Seems like an ugly way to do it though. [added later] I tried adding the second field, containing the first 12 characters from the main field, lowercased. It wasn't a big success. Previously, the average object size was 50 bytes, but I forgot that includes the _id and other overheads, so my main field length (there was only one) averaged nearer to 30 bytes than 50. Then, the second field index contains the _id and other overheads. Net result (for my 8% sample) is the index on the main field is 415MB and on the 12 byte field is 330MB - only a 20% saving in space, not worthwhile. I could duplicate the entire field (to work around the case insensitive search problem) but realistically it looks like I should reconsider whether MongoDB is the right tool for the job (or just buy more memory and use twice as much disk space). [added even later] This is a typical document, with the source field, and the short lowercased field: { "_id" : ObjectId("505d0e89f56588f20f000041"), "q" : "Continental Airlines", "f" : "continental " } Indexes: db.test.ensureIndex({q:1}); db.test.ensureIndex({f:1}); The 'f" index, working on a shorter field, is 80% of the size of the "q" index. I didn't mean to imply I included the _id in the index, just that it needs to use that somewhere to show where the index will point to, so it's an overhead that probably helps explain why a shorter key makes so little difference. Access to the index will be essentially random, no part of it is more likely to be accessed than any other. Total index size for the full file will likely be 5GB, so it's not extreme for that one index. Adding some other fields for other search cases, and their associated indexes, and copies of data for lower case, does start to add up, which I why I started looking into a more concise index.

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• mongodb java driver - raw command?

  - by Scott

  Is it possible to execute raw commands as javascript through the Java driver for MongoDB? I'm tired of wrapping everything in Java objects using Rhino, and would happily sacrifice performance for the convenience of passing javascript directly through to the DB. If not, I can always use sleepymongoose or something, but I don't really want to add yet another language (python) to the stack at this point. Any insights are appreciated.

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• MongoDB using NOT and AND together

  - by Stankalank

  I'm trying to negate an $and clause with MongoDB and I'm getting a MongoError: invalid operator: $and message back. Basically what I want to achieve is the following: query = { $not: { $and: [{institution_type:'A'}, {type:'C'}] } } Is this possible to express in a mongo query? Here is a sample collection: { "institution_type" : "A", "type" : "C" } { "institution_type" : "A", "type" : "D" } { "institution_type" : "B", "type" : "C" } { "institution_type" : "B", "type" : "D" } What I want to get back is the following: { "institution_type" : "A", "type" : "D" } { "institution_type" : "B", "type" : "C" } { "institution_type" : "B", "type" : "D" }

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• Which of CouchDB or MongoDB suits my needs?

  - by vonconrad

  Where I work, we use Ruby on Rails to create both backend and frontend applications. Usually, these applications interact with the same MySQL database. It works great for a majority of our data, but we have one situation which I would like to move to a NoSQL environment. We have clients, and our clients have what we call "inventories"--one or more of them. An inventory can have many thousands of items. This is currently done through two relational database tables, inventories and inventory_items. The problems start when two different inventories have different parameters: # Inventory item from inventory 1, televisions { inventory_id: 1 sku: 12345 name: Samsung LCD 40 inches model: 582903-4 brand: Samsung screen_size: 40 type: LCD price: 999.95 } # Inventory item from inventory 2, accomodation { inventory_id: 2 sku: 48cab23fa name: New York Hilton accomodation_type: hotel star_rating: 5 price_per_night: 395 } Since we obviously can't use brand or star_rating as the column name in inventory_items, our solution so far has been to use generic column names such as text_a, text_b, float_a, int_a, etc, and introduce a third table, inventory_schemas. The tables now look like this: # Inventory schema for inventory 1, televisions { inventory_id: 1 int_a: sku text_a: name text_b: model text_c: brand int_b: screen_size text_d: type float_a: price } # Inventory item from inventory 1, televisions { inventory_id: 1 int_a: 12345 text_a: Samsung LCD 40 inches text_b: 582903-4 text_c: Samsung int_a: 40 text_d: LCD float_a: 999.95 } This has worked well... up to a point. It's clunky, it's unintuitive and it lacks scalability. We have to devote resources to set up inventory schemas. Using separate tables is not an option. Enter NoSQL. With it, we could let each and every item have their own parameters and still store them together. From the research I've done, it certainly seems like a great alterative for this situation. Specifically, I've looked at CouchDB and MongoDB. Both look great. However, there are a few other bits and pieces we need to be able to do with our inventory: We need to be able to select items from only one (or several) inventories. We need to be able to filter items based on its parameters (eg. get all items from inventory 2 where type is 'hotel'). We need to be able to group items based on parameters (eg. get the lowest price from items in inventory 1 where brand is 'Samsung'). We need to (potentially) be able to retrieve thousands of items at a time. We need to be able to access the data from multiple applications; both backend (to process data) and frontend (to display data). Rapid bulk insertion is desired, though not required. Based on the structure, and the requirements, are either CouchDB or MongoDB suitable for us? If so, which one will be the best fit? Thanks for reading, and thanks in advance for answers. EDIT: One of the reasons I like CouchDB is that it would be possible for us in the frontend application to request data via JavaScript directly from the server after page load, and display the results without having to use any backend code whatsoever. This would lead to better page load and less server strain, as the fetching/processing of the data would be done client-side.

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• Outgrew MongoDB … now what?

  - by samsmith

  We dump debug and transaction logs into mongodb. We really like mongodb because: Blazing insert perf document oriented Ability to let the engine drop inserts when needed for performance But there is this big problem with mongodb: The index must fit in physical RAM. In practice, this limits us to 80-150gb of raw data (we currently run on a system with 16gb RAM). Sooooo, for us to have 500gb or a tb of data, we would need 50gb or 80gb of RAM. Yes, I know this is possible. We can add servers and use mongo sharding. We can buy a special server box that can take 100 or 200 gb of RAM, but this is the tail wagging the dog! We could spend boucoup $$$ on hardware to run FOSS, when SQL Server Express can handle WAY more data on WAY less hardware than Mongo (SQL Server does not meet our architectural desires, or we would use it!) We are not going to spend huge $ on hardware here, because it is necessary only because of the Mongo architecture, not because of the inherent processing/storage needs. (And sharding? Please! Cost aside, who needs the ongoing complexity of three, five, or more servers to manage a relatively small load?) Bottom line: MongoDB is FOSS, but we gotta spend $$$$$$$ on hardware to run it? We sould rather buy commercial SW! I am sure we are not the first to hit this issue, so we ask the community: Where do we go next? (We already run Mongo v2) Thanks!!

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• MongoDb vs Ehcache caching advise for speeding up read only mysql Database

  - by paddydub

  I'm building a Route Planner Webapp using Spring/Hibernate/Tomcat and a mysql database, I have a database containing read only data, such as Bus Stop Coordinates, Bus times which is never updated. I'm trying to make the app run faster, each time the application is run it will preform approx 1000 reads to the database to calculate a route. I have setup a Ehcache which greatly improves the read from database times. I'm now setting terracotta + Ehcache distributed caching to share the cache with multiple Tomcat JVMs. This seems a bit complicated. I've tried memcached but it was not performing as fast as ehcache. I'm wondering if a MongoDb would be better suited. I have no experience with nosql but I would appreciate if anyone has any ideas. All i need is quick access to the read only database.

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• Middleware for MongoDB or CouchDB with jQuery Ajax/JSON frontend

  - by Tauren

  I've been using the following web development stack for a few years: java/spring/hibernate/mysql/jetty/wicket/jquery For certain requirements, I'm considering switching to a NoSQL datastore with an AJAX frontend. I would probably build the frontend with jQuery and communicate with the web application middleware using JSON. I'm leaning toward MongoDB because of more dynamic query capabilities, but am still considering CouchDB. I'm not sure what to use in the middle. Probably something RESTful? My preference is to stick with Java (or maybe Scala or Groovy) since I'm using tools like Drools for rules and Shiro for security. But then again, I want to pick something that is quick an easy to work with, so I'm open to other solutions. If you are building ajax/json/nosql solutions, I'd like to hear details about what tools you are using and any pros/cons you've found to using them. Thanks!

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• How to get the ObjectId value from MongoDB?

  - by LVarayut

  I'm using Jongo with Play framework 2, java. I added some data into my MongoDB. {"_id" : ObjectId("538dafffbf6b562617252178"), ... } However, when I fetched the ObjectId from the database, it gave me like: de.undercouch.bson4jackson.types.ObjectId@484431ff instead of 538dafffbf6b562617252178. I don't quite understand how can I get the ObjectId value. My class is defined as following: public class Product { @JsonProperty("_id") protected String id; ... public Product() { } public String getId() { return id; } public void setId(String id) { this.id = id; } } EDIT In order to fetch the data, I simply use find() function provided by Jongo as following: public static Iterable<Product> findAll(){ return products().find().as(Product.class); }

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• How to "defragment" MongoDB index effectively in production?

  - by dfrankow

  I've been looking at MongoDB. Feels good. I added some indexes to a collection, uploaded a bunch of data, then removed all the data, and I noticed the indexes did not change size, similar to the behavior reported here. If I call db.repairDatabase() the indexes are then squashed to near-zero. Similarly if I don't remove all the data, but call repairDatabase(), the indexes are squashed somewhat (perhaps because unused extends are truncated?). I am getting index size from "totalIndexSize" of db.collection.stats(). However, that takes a long time (I've read it could be hours on a large database). It's unclear to me how available the database is for reads or writes while it is running. I am guessing not so available. Since I want to run as few instances of mongod as possible, I want to understand more about how indexes are managed after deletes. Can anyone point me to anything or give any advice?

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• MongoDB query to return only embedded document

  - by Matt

  assume that i have a BlogPost model with zero-to-many embedded Comment documents. can i query for and have MongoDB return only Comment objects matching my query spec? eg, db.blog_posts.find({"comment.submitter": "some_name"}) returns only a list of comments. edit: an example: import pymongo connection = pymongo.Connection() db = connection['dvds'] db['dvds'].insert({'title': "The Hitchhikers Guide to the Galaxy", 'episodes': [{'title': "Episode 1", 'desc': "..."}, {'title': "Episode 2", 'desc': "..."}, {'title': "Episode 3", 'desc': "..."}, {'title': "Episode 4", 'desc': "..."}, {'title': "Episode 5", 'desc': "..."}, {'title': "Episode 6", 'desc': "..."}]}) episode = db['dvds'].find_one({'episodes.title': "Episode 1"}, fields=['episodes']) in this example, episode is: {u'_id': ObjectId('...'), u'episodes': [{u'desc': u'...', u'title': u'Episode 1'}, {u'desc': u'...', u'title': u'Episode 2'}, {u'desc': u'...', u'title': u'Episode 3'}, {u'desc': u'...', u'title': u'Episode 4'}, {u'desc': u'...', u'title': u'Episode 5'}, {u'desc': u'...', u'title': u'Episode 6'}]} but i just want: {u'desc': u'...', u'title': u'Episode 1'}

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• Finding changes in MongoDB database

  - by Jonathan Knight

  I'm designing a MongoDB database that works with a script that periodically polls a resource and gets back a response which is stored in the database. Right now my database has one collection with four fields , id, name, timestamp and data. I need to be able to find out which names had changes in the data field between script runs, and which did not. In pseudocode, if(data[name][timestamp]==data[name][timestamp+1]) //data has not changed store data in collection 1 else //data has changed between script runs for this name store data in collection 2 Is there a query that can do this without iterating and running javascript over each item in the collection? There are millions of documents, so this would be pretty slow. Should I create a new collection named timestamp for every time the script runs? Would that make it faster/more organized? Is there a better schema that could be used? The script runs once a day so I won't run into a namespace limitation any time soon.

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• MongoDB map/reduce counts

  - by ibz

  The output from MongoDB's map/reduce includes something like 'counts': {'input': I, 'emit': E, 'output': O}. I thought I clearly understand what those mean, until I hit a weird case which I can't explain. According to my understanding, counts.input is the number of rows that match the condition (as specified in query). If so, how is it possible that the following two queries have different results? db.mycollection.find({MY_CONDITION}).count() db.mycollection.mapReduce(SOME_MAP, SOME_REDUCE, {'query': {MY_CONDITION}}).counts.input I thought the two should always give the same result, independent of the map and reduce functions, as long as the same condition is used.

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• Adding a MongoDB collection to Netbeans

  - by Saif Bechan

  In Netbeans I have an option to add my mysql databases to netbeans. This way I can easily browse and so small queries. Now I am working on a MongoDB project, and I want to know if it is possible to use the same functionality. I see that on the website of mongo there is a list of drivers, and I see that you can add drivers in netbeans. I do not know if the same thing, or if this can be used. I have tried google, but no luck. Anyone have an idea?

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• Are MongoDB and CouchDB perfect substitutes?

  - by raoulsson

  I haven't got my hands dirty yet with neither CouchDB nor MongoDB but I would like to do so soon... I also have read a bit about both systems and it looks to me like they cover the same cases... Or am I missing a key distinguishing feature? I would like to use a document based storage instead of a traditional RDBMS in my next project. I also need the datastore to handle large binary objects (images and videos) automatically replicate itself to physically separate nodes rendering the need of an additional RDBMS superfluous Are both equally well suited for these requirements? Thanks!

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• How to organise a many to many relationship in MongoDB

  - by Gareth Elms

  I have two tables/collections; Users and Groups. A user can be a member of any number of groups and a user can also be an owner of any number of groups. In a relational database I'd probably have a third table called UserGroups with a UserID column, a GroupID column and an IsOwner column. I'm using MongoDB and I'm sure there is a different approach for this kind of relationship in a document database. Should I embed the list of groups and groups-as-owner inside the Users table as two arrays of ObjectIDs? Should I also store the list of members and owners in the Groups table as two arrays, effectively mirroring the relationship causing a duplication of relationship information? Or is a bridging UserGroups table a legitimate concept in document databases for many to many relationships? Thanks

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• Find objects between two dates MongoDB

  - by Tom

  I've been playing around storing tweets inside mongodb, each object looks like this: { "_id" : ObjectId("4c02c58de500fe1be1000005"), "contributors" : null, "text" : "Hello world", "user" : { "following" : null, "followers_count" : 5, "utc_offset" : null, "location" : "", "profile_text_color" : "000000", "friends_count" : 11, "profile_link_color" : "0000ff", "verified" : false, "protected" : false, "url" : null, "contributors_enabled" : false, "created_at" : "Sun May 30 18:47:06 +0000 2010", "geo_enabled" : false, "profile_sidebar_border_color" : "87bc44", "statuses_count" : 13, "favourites_count" : 0, "description" : "", "notifications" : null, "profile_background_tile" : false, "lang" : "en", "id" : 149978111, "time_zone" : null, "profile_sidebar_fill_color" : "e0ff92" }, "geo" : null, "coordinates" : null, "in_reply_to_user_id" : 149183152, "place" : null, "created_at" : "Sun May 30 20:07:35 +0000 2010", "source" : "web", "in_reply_to_status_id" : { "floatApprox" : 15061797850 }, "truncated" : false, "favorited" : false, "id" : { "floatApprox" : 15061838001 } How would I write a query which checks the *created_at* and finds all objects between 18:47 and 19:00? Do I need to update my documents so the dates are stored in a specific format? Thanks

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• Get averages from pre-aggregated reports in mongodb

  - by Chris

  I've got a database with pre-aggregated metrics similar to the one outlined in this use case: http://docs.mongodb.org/manual/use-cases/pre-aggregated-reports/ I have a daily collection with a subdocument for hour and minute metrics, and a 'metadata.date' entry for midnight on the day it represents. I also have a monthly collection with a day subdocument for each day. If I want to get an average of a metric over the past eight or so days how can I do that with the aggregation framework? Is the aggregation framework not the right tool for this since it's already pre-aggregated?

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• Squid logs on mongodb

  - by user306241

  Hi, I'm planning to log my squid instances to a mongodb, but the actual problem is that we have a huge traffic to be logged, every access authenticated with user/pass. Eventually we have to make some reports based on logs. I was thinking to insert the logs distributed by months and by users, so my collection will look like this: {month: 'april', users: [{user: 'loop0', logs: [{timestamp: 12345678.9, url: 'http://stackoverflow.com/question/ask', ... }]}] So if I want to generate my reports based on the month of april I just have to get the right month instead of looking in zillions of lines to fetch the lines that timestamp match between April, 1 and April, 30. Of course this type of insert will be slower than just insert the log line directly. So my question is: is there a best way to do this? Nowadays we have around 12 million lines of log by day.

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• How would you represent an object that can be of multiple types, when storing it as a document in MongoDB?

  - by blueberryfields

  Somewhat related to this question, say that I have an object category which, depending on which type of object I have, has different restrictions on what it contains. (if you can reword the previous sentence to make more sense I'd appreciate it) For example var SomeSchema = new Schema({ _id: ObjectID, [... large number of common fields ...] type: //Restricted somehow to members of a fixed array data: //depending on type, this might be restricted - sometimes an integer, sometimes array, etc... }); What's the idiosyncratic method for defining this type of schema? Is it appropriate to define a single schema, and handle the types inside of it's members, or am I better off with separate schema for each type?

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• mongodb: insert if not exists

  - by LeMiz

  Hello, Every day, I receive a stock of documents (an update). What I want to do is inserting each of them if it does not exists. I also want to keep track of the first time I inserted them, and the last time I saw them in an update. I don't want to have duplicate documents. I don't want to remove a document which has previously been saved, but is not in my update. 95% (estimated) of the records are unmodified from day to day. I am using the python driver (pymongo), for that matter. What I currently do is (pseudo-code): for each document in update: existing_document = collection.find_one(document) if not existing_document: document['insertion_date'] = now else: document = existing_document document['last_update_date'] = now my_collection.save(document) My problem is that it is very slow (40 mins for less than 100 000 records, and I have millions of them in the update). I am pretty sure there is something builtin for doing this, but the document for update() is mmmhhh.... a bit terse.... ( http://www.mongodb.org/display/DOCS/Updating ) Can someone give an advice on doing it faster ?

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• Child objects in MongoDB

  - by Jeremy B.

  I have been following along with Rob Conery's Linq for MongoDB and have come across a question. In the example he shows how you can easily nest a child object. For my current experiment I have the following structure. class Content { ... Profile Profile { get; set; } } class Profile { ... } This works great when looking at content items. The dilemma I'm facing now is if I want to treat the Profile as an atomic object. As it stands, it appears as if I can not query the Profile object directly but that it comes packaged with Content results. If I want it to be inclusive, but also be able to query on just Profile I feel like my first instinct would be to make Profiles a top level object and then create a foreign key like structure under the Content class to tie the two together. To me it feels like I'm falling back on RDBMS practices and that feels like I'm most likely going against the spirit of Mongo. How would you treat an object you need to act upon independently yet also want as a child object of another object?

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• Filtering documents against a dictionary key in MongoDB

  - by Thomas

  I have a collection of articles in MongoDB that has the following structure: { 'category': 'Legislature', 'updated': datetime.datetime(2010, 3, 19, 15, 32, 22, 107000), 'byline': None, 'tags': { 'party': ['Peter Hoekstra', 'Virg Bernero', 'Alma Smith', 'Mike Bouchard', 'Tom George', 'Rick Snyder'], 'geography': ['Michigan', 'United States', 'North America'] }, 'headline': '2 Mich. gubernatorial candidates speak to students', 'text': [ 'BEVERLY HILLS, Mich. (AP) \u2014 Two Democratic and Republican gubernatorial candidates found common ground while speaking to private school students in suburban Detroit', "Democratic House Speaker state Rep. Andy Dillon and Republican U.S. Rep. Pete Hoekstra said Friday a more business-friendly government can help reduce Michigan's nation-leading unemployment rate.", "The candidates were invited to Detroit Country Day Upper School in Beverly Hills to offer ideas for Michigan's future.", 'Besides Dillon, the Democratic field includes Lansing Mayor Virg Bernero and state Rep. Alma Wheeler Smith. Other Republicans running are Oakland County Sheriff Mike Bouchard, Attorney General Mike Cox, state Sen. Tom George and Ann Arbor business leader Rick Snyder.', 'Former Republican U.S. Rep. Joe Schwarz is considering running as an independent.' ], 'dateline': 'BEVERLY HILLS, Mich.', 'published': datetime.datetime(2010, 3, 19, 8, 0, 31), 'keywords': "Governor's Race", '_id': ObjectId('4ba39721e0e16cb25fadbb40'), 'article_id': 'urn:publicid:ap.org:0611e36fb084458aa620c0187999db7e', 'slug': "BC-MI--Governor's Race,2nd Ld-Writethr" } If I wanted to write a query that looked for all articles that had at least 1 geography tag, how would I do that? I have tried writing db.articles.find( {'tags': 'geography'} ), but that doesn't appear to work. I've also thought about changing the search parameter to 'tags.geography', but am having a devil of a time figuring out what the search predicate would be.

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